The integration of IoT and Deep Learning (DL) has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management (PHM). Electrocardiograms (ECGs) are widely used for cardiovascular disease (CVD) diagnosis, but fluctuating signal patterns make classification challenging. Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations. With this motivation, the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis. Deep Transfer Learning (DTL) techniques extract features, followed by feature fusion to eliminate redundancy and retain the most informative features. Utilizing the African Vulture Optimization Algorithm (AVOA) for feature selection is more effective than the standard methods, as it offers an ideal balance between exploration and exploitation that results in an optimal set of features, improving classification performance while reducing redundancy. Various machine learning classifiers, including Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extreme Learning Machine (ELM), are used for further classification. Additionally, an ensemble model is developed to further improve accuracy. Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%, highlighting its effectiveness in enhancing CVD diagnosis.
This study investigates the mechanical behavior of polyurethane (PU) nanocomposites reinforced with nanodiamonds (NDs) and proposes an integrated optimization–prediction framework that combines the Taguchi method with machine learning (ML). The Taguchi design of experiments (DOE), based on an L9 orthogonal array, was applied to investigate the influence of composite type (pure PU, 0.1 wt.% ND, 0.5 wt.% ND), temperature (145°C–165°C), screw speed (50–70 rpm), and pressure (40–60 bar). The mechanical tests included tensile, hardness, and modulus measurements, performed under varying process parameters. Results showed that the addition of 0.5 wt.% ND substantially improved PU performance, with tensile strength increasing by 117%, Young’s modulus by 10%, and hardness by 21% at optimal conditions of 145°C, 70 rpm, and 50 bar. SEM analysis revealed ductile fracture in pure PU and brittle fracture in the optimized PU/ND composite. ANOVA confirmed that composite type was the most influential factor, contributing 70.27%, 87.14%, and 74.16% to tensile strength, modulus, and hardness, respectively. Regression modeling demonstrated a deviation of less than 10% between predicted and experimental values, validating the framework. To further strengthen predictive capability, computational modeling and analytical procedures were employed through machine learning frameworks. Random Forest achieved R 2 /MSE values of 0.95/0.53 (tensile), 0.95/4.03 (modulus), and 0.94/2.44 (hardness). XGBoost performed better, with 0.98/0.12, 0.98/0.77, and 0.98/0.60, while Gradient Boosting provided the highest accuracy with 0.99/0.03, 0.99/0.02, and 0.99/0.01. Residual plots supported these results, showing wide fluctuations for RF and tightly clustered residuals near zero for GB and XGB, highlighting their superior accuracy, precision, and generalization. Overall, the integrated Taguchi–ML framework demonstrates a robust and efficient strategy for optimizing processing parameters and accurately predicting the performance of high-strength PU–ND nanocomposites.